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Details	EV-TRACK ID	Experiment nr.	Species	Sample type	Separation protocol	First author	Year	EV-METRIC
	EV200000	3/4	Homo sapiens	pleural effusion	(d)(U)C	Ping, Luo	2020	67%
Study summary Full title Metabolic characteristics of large and small extracellular vesicles from pleural effusion reveal biomarker candidates for the diagnosis of tuberculosis and malignancy All authors Ping Luo, Kaimin Mao, Juanjuan Xu, Feng Wu, Xuan Wang, Sufei Wang, Mei Zhou, Limin Duan, Qi Tan, Guangzhou Ma, Guanghai Yang, Ronghui Du, Hai Huang, Qi Huang, Yumei Li, Mengfei Guo, Yang Jin Journal J Extracell Vesicles Abstract Pleural effusion is a common respiratory disease worldwide; however, rapid and accurate diagnoses of (show more...)Pleural effusion is a common respiratory disease worldwide; however, rapid and accurate diagnoses of tuberculosis pleural effusion (TPE) and malignancy pleural effusion (MPE) remain challenging. Although extracellular vesicles (EVs) have been confirmed as promising sources of disease biomarkers, little is known about the metabolite compositions of its subpopulations and their roles in the diagnosis of pleural effusion. Here, we performed metabolomics and lipidomics analysis to investigate the metabolite characteristics of two EV subpopulations derived from pleural effusion by differential ultracentrifugation, namely large EVs (lEVs, pelleted at 20,000 × g) and small EVs (sEVs, pelleted at 110,000 × g), and assessed their metabolite differences between tuberculosis and malignancy. A total of 579 metabolites, including amino acids, acylcarnitines, organic acids, steroids, amides and various lipid species, were detected. The results showed that the metabolic profiles of lEVs and sEVs overlapped with and difference from each other but significantly differed from those of pleural effusion. Additionally, different type of vesicles and pleural effusion showed unique metabolic enrichments. Furthermore, lEVs displayed more significant and larger metabolic alterations between the tuberculosis and malignancy groups, and their differential metabolites were more closely related to clinical parameters than those of sEV. Finally, a panel of four biomarker candidates, including phenylalanine, leucine, phosphatidylcholine 35:0, and sphingomyelin 44:3, in pleural lEVs was defined based on the comprehensive discovery and validation workflow. This panel showed high performance for distinguishing TPE and MPE, particularly in patients with delayed or missed diagnosis, such as the area under the receiver-operating characteristic curve (AUC) >0.95 in both sets. We conducted comprehensive metabolic profiling analysis of EVs, and further explored the metabolic reprogramming of tuberculosis and malignancy at the level of metabolites in lEVs and sEVs, providing insight into the mechanism of pleural effusion, and identifying novel biomarkers for diagnosing TPE and MPE. (hide) EV-METRIC 67% (90th percentile of all experiments on the same sample type) Reported Not reported Not applicable EV-enriched proteins Protein analysis: analysis of three or more EV-enriched proteins non EV-enriched protein Protein analysis: assessment of a non-EV-enriched protein qualitative and quantitative analysis Particle analysis: implementation of both qualitative and quantitative methods. For the quantitative method, the reporting of measured EV concentration is expected. electron microscopy images Particle analysis: inclusion of a widefield and close-up electron microscopy image density gradient Separation method: density gradient, at least as validation of results attributed to EVs EV density Separation method: reporting of obtained EV density ultracentrifugation specifics Separation method: reporting of g-forces, duration and rotor type of ultracentrifugation steps antibody specifics Protein analysis: antibody clone/reference number and dilution lysate preparation Protein analysis: lysis buffer composition Study data Sample type pleural effusion Sample origin lung cancer Focus vesicles Other / small extracellular vesicles Separation protocol Separation protocol Gives a short, non-chronological overview of the different steps of the separation protocol. dUC = (Differential) (ultra)centrifugation DG = density gradient UF = ultrafiltration SEC = size-exclusion chromatography IAF = immuno-affinity capture (d)(U)C Protein markers EV: CD81/ TSG101/ CD63/ CD9 non-EV: calnexin/ GM130 Proteomics no Show all info Study aim Biomarker/Identification of content (omics approaches) Sample Species Homo sapiens Sample Type pleural effusion Separation Method (Differential) (ultra)centrifugation dUC: centrifugation steps Below or equal to 800 g Between 800 g and 10,000 g Between 10,000 g and 50,000 g Between 100,000 g and 150,000 g Pelleting performed Yes Pelleting: time(min) 90 Pelleting: rotor type Type 70 Ti Pelleting: speed (g) 110000 Wash: volume per pellet (ml) 30 Wash: time (min) 90 Wash: Rotor Type Type 70 Ti Wash: speed (g) 110000 EV-subtype Distinction between multiple subtypes Size Used subtypes 200-1000 nm Characterization: Protein analysis Protein Concentration Method BCA Western Blot Antibody details provided? Yes Antibody dilution provided? Yes Lysis buffer provided? Yes Detected EV-associated proteins CD9/ CD63/ TSG101 Not detected EV-associated proteins CD81 Not detected contaminants calnexin/ GM130 Characterization: Lipid analysis Yes Characterization: Particle analysis NTA Report type Mean Reported size (nm) 252.3 EV concentration Yes Particle yield Yes, as number of particles per milliliter of starting sample 199903278 EM EM-type Transmission-EM Image type Close-up

	EV200000	4/4	Homo sapiens	pleural effusion	(d)(U)C	Ping, Luo	2020	67%
Study summary Full title Metabolic characteristics of large and small extracellular vesicles from pleural effusion reveal biomarker candidates for the diagnosis of tuberculosis and malignancy All authors Ping Luo, Kaimin Mao, Juanjuan Xu, Feng Wu, Xuan Wang, Sufei Wang, Mei Zhou, Limin Duan, Qi Tan, Guangzhou Ma, Guanghai Yang, Ronghui Du, Hai Huang, Qi Huang, Yumei Li, Mengfei Guo, Yang Jin Journal J Extracell Vesicles Abstract Pleural effusion is a common respiratory disease worldwide; however, rapid and accurate diagnoses of (show more...)Pleural effusion is a common respiratory disease worldwide; however, rapid and accurate diagnoses of tuberculosis pleural effusion (TPE) and malignancy pleural effusion (MPE) remain challenging. Although extracellular vesicles (EVs) have been confirmed as promising sources of disease biomarkers, little is known about the metabolite compositions of its subpopulations and their roles in the diagnosis of pleural effusion. Here, we performed metabolomics and lipidomics analysis to investigate the metabolite characteristics of two EV subpopulations derived from pleural effusion by differential ultracentrifugation, namely large EVs (lEVs, pelleted at 20,000 × g) and small EVs (sEVs, pelleted at 110,000 × g), and assessed their metabolite differences between tuberculosis and malignancy. A total of 579 metabolites, including amino acids, acylcarnitines, organic acids, steroids, amides and various lipid species, were detected. The results showed that the metabolic profiles of lEVs and sEVs overlapped with and difference from each other but significantly differed from those of pleural effusion. Additionally, different type of vesicles and pleural effusion showed unique metabolic enrichments. Furthermore, lEVs displayed more significant and larger metabolic alterations between the tuberculosis and malignancy groups, and their differential metabolites were more closely related to clinical parameters than those of sEV. Finally, a panel of four biomarker candidates, including phenylalanine, leucine, phosphatidylcholine 35:0, and sphingomyelin 44:3, in pleural lEVs was defined based on the comprehensive discovery and validation workflow. This panel showed high performance for distinguishing TPE and MPE, particularly in patients with delayed or missed diagnosis, such as the area under the receiver-operating characteristic curve (AUC) >0.95 in both sets. We conducted comprehensive metabolic profiling analysis of EVs, and further explored the metabolic reprogramming of tuberculosis and malignancy at the level of metabolites in lEVs and sEVs, providing insight into the mechanism of pleural effusion, and identifying novel biomarkers for diagnosing TPE and MPE. (hide) EV-METRIC 67% (90th percentile of all experiments on the same sample type) Reported Not reported Not applicable EV-enriched proteins Protein analysis: analysis of three or more EV-enriched proteins non EV-enriched protein Protein analysis: assessment of a non-EV-enriched protein qualitative and quantitative analysis Particle analysis: implementation of both qualitative and quantitative methods. For the quantitative method, the reporting of measured EV concentration is expected. electron microscopy images Particle analysis: inclusion of a widefield and close-up electron microscopy image density gradient Separation method: density gradient, at least as validation of results attributed to EVs EV density Separation method: reporting of obtained EV density ultracentrifugation specifics Separation method: reporting of g-forces, duration and rotor type of ultracentrifugation steps antibody specifics Protein analysis: antibody clone/reference number and dilution lysate preparation Protein analysis: lysis buffer composition Study data Sample type pleural effusion Sample origin lung cancer Focus vesicles Other / small extracellular vesicles Separation protocol Separation protocol Gives a short, non-chronological overview of the different steps of the separation protocol. dUC = (Differential) (ultra)centrifugation DG = density gradient UF = ultrafiltration SEC = size-exclusion chromatography IAF = immuno-affinity capture (d)(U)C Protein markers EV: TSG101/ CD81/ CD63/ CD9 non-EV: calnexin/ GM130 Proteomics no Show all info Study aim Biomarker/Identification of content (omics approaches) Sample Species Homo sapiens Sample Type pleural effusion Separation Method (Differential) (ultra)centrifugation dUC: centrifugation steps Below or equal to 800 g Between 800 g and 10,000 g Between 10,000 g and 50,000 g Between 100,000 g and 150,000 g Pelleting performed Yes Pelleting: time(min) 90 Pelleting: rotor type Type 70 Ti Pelleting: speed (g) 110000 Wash: volume per pellet (ml) 30 Wash: time (min) 90 Wash: Rotor Type Type 70 Ti Wash: speed (g) 110000 EV-subtype Distinction between multiple subtypes Size Used subtypes 50-200 nm Characterization: Protein analysis Protein Concentration Method BCA Western Blot Antibody details provided? Yes Antibody dilution provided? Yes Lysis buffer provided? Yes Detected EV-associated proteins CD9/ CD63/ TSG101/ CD81 Not detected contaminants calnexin/ GM130 Characterization: Lipid analysis Yes Characterization: Particle analysis NTA Report type Mean Reported size (nm) 136.4 EV concentration Yes Particle yield Yes, as number of particles per milliliter of starting sample 168611013 EM EM-type Transmission-EM Image type Close-up

	EV200000	1/4	Homo sapiens	pleural effusion	(d)(U)C	Ping, Luo	2020	56%
Study summary Full title Metabolic characteristics of large and small extracellular vesicles from pleural effusion reveal biomarker candidates for the diagnosis of tuberculosis and malignancy All authors Ping Luo, Kaimin Mao, Juanjuan Xu, Feng Wu, Xuan Wang, Sufei Wang, Mei Zhou, Limin Duan, Qi Tan, Guangzhou Ma, Guanghai Yang, Ronghui Du, Hai Huang, Qi Huang, Yumei Li, Mengfei Guo, Yang Jin Journal J Extracell Vesicles Abstract Pleural effusion is a common respiratory disease worldwide; however, rapid and accurate diagnoses of (show more...)Pleural effusion is a common respiratory disease worldwide; however, rapid and accurate diagnoses of tuberculosis pleural effusion (TPE) and malignancy pleural effusion (MPE) remain challenging. Although extracellular vesicles (EVs) have been confirmed as promising sources of disease biomarkers, little is known about the metabolite compositions of its subpopulations and their roles in the diagnosis of pleural effusion. Here, we performed metabolomics and lipidomics analysis to investigate the metabolite characteristics of two EV subpopulations derived from pleural effusion by differential ultracentrifugation, namely large EVs (lEVs, pelleted at 20,000 × g) and small EVs (sEVs, pelleted at 110,000 × g), and assessed their metabolite differences between tuberculosis and malignancy. A total of 579 metabolites, including amino acids, acylcarnitines, organic acids, steroids, amides and various lipid species, were detected. The results showed that the metabolic profiles of lEVs and sEVs overlapped with and difference from each other but significantly differed from those of pleural effusion. Additionally, different type of vesicles and pleural effusion showed unique metabolic enrichments. Furthermore, lEVs displayed more significant and larger metabolic alterations between the tuberculosis and malignancy groups, and their differential metabolites were more closely related to clinical parameters than those of sEV. Finally, a panel of four biomarker candidates, including phenylalanine, leucine, phosphatidylcholine 35:0, and sphingomyelin 44:3, in pleural lEVs was defined based on the comprehensive discovery and validation workflow. This panel showed high performance for distinguishing TPE and MPE, particularly in patients with delayed or missed diagnosis, such as the area under the receiver-operating characteristic curve (AUC) >0.95 in both sets. We conducted comprehensive metabolic profiling analysis of EVs, and further explored the metabolic reprogramming of tuberculosis and malignancy at the level of metabolites in lEVs and sEVs, providing insight into the mechanism of pleural effusion, and identifying novel biomarkers for diagnosing TPE and MPE. (hide) EV-METRIC 56% (72nd percentile of all experiments on the same sample type) Reported Not reported Not applicable EV-enriched proteins Protein analysis: analysis of three or more EV-enriched proteins non EV-enriched protein Protein analysis: assessment of a non-EV-enriched protein qualitative and quantitative analysis Particle analysis: implementation of both qualitative and quantitative methods. For the quantitative method, the reporting of measured EV concentration is expected. electron microscopy images Particle analysis: inclusion of a widefield and close-up electron microscopy image density gradient Separation method: density gradient, at least as validation of results attributed to EVs EV density Separation method: reporting of obtained EV density ultracentrifugation specifics Separation method: reporting of g-forces, duration and rotor type of ultracentrifugation steps antibody specifics Protein analysis: antibody clone/reference number and dilution lysate preparation Protein analysis: lysis buffer composition Study data Sample type pleural effusion Sample origin tuberculosis Focus vesicles Other / small extracellular vesicles Separation protocol Separation protocol Gives a short, non-chronological overview of the different steps of the separation protocol. dUC = (Differential) (ultra)centrifugation DG = density gradient UF = ultrafiltration SEC = size-exclusion chromatography IAF = immuno-affinity capture (d)(U)C Protein markers EV: TSG101/ CD81/ CD63/ CD9 non-EV: calnexin/ GM130 Proteomics no Show all info Study aim Biomarker/Identification of content (omics approaches) Sample Species Homo sapiens Sample Type pleural effusion Separation Method (Differential) (ultra)centrifugation dUC: centrifugation steps Below or equal to 800 g Between 800 g and 10,000 g Between 10,000 g and 50,000 g Between 100,000 g and 150,000 g Pelleting performed Yes Pelleting: time(min) 90 Pelleting: rotor type Type 70 Ti Pelleting: speed (g) 110000 Wash: volume per pellet (ml) 30 Wash: time (min) 90 Wash: Rotor Type Type 70 Ti Wash: speed (g) 110000 EV-subtype Distinction between multiple subtypes Size Used subtypes 50-200 nm Characterization: Protein analysis Protein Concentration Method BCA Western Blot Antibody details provided? Yes Antibody dilution provided? Yes Lysis buffer provided? Yes Detected EV-associated proteins CD63/ TSG101/ CD81 Not detected EV-associated proteins CD9 Not detected contaminants calnexin/ GM130 Characterization: Lipid analysis Yes Characterization: Particle analysis NTA Report type Mean Reported size (nm) 136.1 EV concentration Yes Particle yield Yes, as number of particles per milliliter of starting sample 182567942

	EV200000	2/4	Homo sapiens	pleural effusion	(d)(U)C	Ping, Luo	2020	56%
Study summary Full title Metabolic characteristics of large and small extracellular vesicles from pleural effusion reveal biomarker candidates for the diagnosis of tuberculosis and malignancy All authors Ping Luo, Kaimin Mao, Juanjuan Xu, Feng Wu, Xuan Wang, Sufei Wang, Mei Zhou, Limin Duan, Qi Tan, Guangzhou Ma, Guanghai Yang, Ronghui Du, Hai Huang, Qi Huang, Yumei Li, Mengfei Guo, Yang Jin Journal J Extracell Vesicles Abstract Pleural effusion is a common respiratory disease worldwide; however, rapid and accurate diagnoses of (show more...)Pleural effusion is a common respiratory disease worldwide; however, rapid and accurate diagnoses of tuberculosis pleural effusion (TPE) and malignancy pleural effusion (MPE) remain challenging. Although extracellular vesicles (EVs) have been confirmed as promising sources of disease biomarkers, little is known about the metabolite compositions of its subpopulations and their roles in the diagnosis of pleural effusion. Here, we performed metabolomics and lipidomics analysis to investigate the metabolite characteristics of two EV subpopulations derived from pleural effusion by differential ultracentrifugation, namely large EVs (lEVs, pelleted at 20,000 × g) and small EVs (sEVs, pelleted at 110,000 × g), and assessed their metabolite differences between tuberculosis and malignancy. A total of 579 metabolites, including amino acids, acylcarnitines, organic acids, steroids, amides and various lipid species, were detected. The results showed that the metabolic profiles of lEVs and sEVs overlapped with and difference from each other but significantly differed from those of pleural effusion. Additionally, different type of vesicles and pleural effusion showed unique metabolic enrichments. Furthermore, lEVs displayed more significant and larger metabolic alterations between the tuberculosis and malignancy groups, and their differential metabolites were more closely related to clinical parameters than those of sEV. Finally, a panel of four biomarker candidates, including phenylalanine, leucine, phosphatidylcholine 35:0, and sphingomyelin 44:3, in pleural lEVs was defined based on the comprehensive discovery and validation workflow. This panel showed high performance for distinguishing TPE and MPE, particularly in patients with delayed or missed diagnosis, such as the area under the receiver-operating characteristic curve (AUC) >0.95 in both sets. We conducted comprehensive metabolic profiling analysis of EVs, and further explored the metabolic reprogramming of tuberculosis and malignancy at the level of metabolites in lEVs and sEVs, providing insight into the mechanism of pleural effusion, and identifying novel biomarkers for diagnosing TPE and MPE. (hide) EV-METRIC 56% (72nd percentile of all experiments on the same sample type) Reported Not reported Not applicable EV-enriched proteins Protein analysis: analysis of three or more EV-enriched proteins non EV-enriched protein Protein analysis: assessment of a non-EV-enriched protein qualitative and quantitative analysis Particle analysis: implementation of both qualitative and quantitative methods. For the quantitative method, the reporting of measured EV concentration is expected. electron microscopy images Particle analysis: inclusion of a widefield and close-up electron microscopy image density gradient Separation method: density gradient, at least as validation of results attributed to EVs EV density Separation method: reporting of obtained EV density ultracentrifugation specifics Separation method: reporting of g-forces, duration and rotor type of ultracentrifugation steps antibody specifics Protein analysis: antibody clone/reference number and dilution lysate preparation Protein analysis: lysis buffer composition Study data Sample type pleural effusion Sample origin tuberculosis Focus vesicles Other / small extracellular vesicles Separation protocol Separation protocol Gives a short, non-chronological overview of the different steps of the separation protocol. dUC = (Differential) (ultra)centrifugation DG = density gradient UF = ultrafiltration SEC = size-exclusion chromatography IAF = immuno-affinity capture (d)(U)C Protein markers EV: CD81/ TSG101/ CD63/ CD9 non-EV: calnexin/ GM130 Proteomics no Show all info Study aim Biomarker/Identification of content (omics approaches) Sample Species Homo sapiens Sample Type pleural effusion Separation Method (Differential) (ultra)centrifugation dUC: centrifugation steps Below or equal to 800 g Between 800 g and 10,000 g Between 10,000 g and 50,000 g Between 100,000 g and 150,000 g Pelleting performed Yes Pelleting: time(min) 90 Pelleting: rotor type Type 70 Ti Pelleting: speed (g) 110000 Wash: volume per pellet (ml) 30 Wash: time (min) 90 Wash: Rotor Type Type 70 Ti Wash: speed (g) 110000 EV-subtype Distinction between multiple subtypes Size Used subtypes 200-1000 nm Characterization: Protein analysis Protein Concentration Method BCA Western Blot Antibody details provided? Yes Antibody dilution provided? Yes Lysis buffer provided? Yes Detected EV-associated proteins CD9/ CD63/ TSG101 Not detected EV-associated proteins CD81 Not detected contaminants calnexin/ GM130 Characterization: Lipid analysis Yes Characterization: Particle analysis NTA Report type Mean Reported size (nm) 224.2 EV concentration Yes Particle yield Yes, as number of particles per milliliter of starting sample 244682871

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EV-TRACK ID	EV200000
species	Homo sapiens
sample type	pleural effusion
condition	lung cancer	lung cancer	tuberculosis	tuberculosis
separation protocol	(d)(U)C	(d)(U)C	(d)(U)C	(d)(U)C
EV subtype	200-1000 nm	50-200 nm	50-200 nm	200-1000 nm
Exp. nr.	3	4	1	2
EV-METRIC %	67	67	56	56